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During the COVID-19 pandemic, most scientific contributions investigated the phenomenon from an associational perspective. For instance, Zhou et al. (2021) investigates the association between PM \(_{2.5}\) levels during the 2020 wildfires and the number of COVID-19 cases and deaths by exploiting innovative data sources such as satellite data. In this short paper, we go beyond association and provide a causal analysis using the data from Zhou et al. (2021). We design the study and exploit the recent Synthetic Control for Staggered Adoption method (Ben-Michael et al., 2022) to estimate the treatment effect on treated units of wildfires on COVID-19 cases. We leave out the analysis on deaths for brevity. Results are encouraging but not conclusive.

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From Association to Causation: The COVID-19 Wildfire Data

  • 会议论文

  • 发表日期：

    2025年

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  • Veronica Ballerini

摘要

During the COVID-19 pandemic, most scientific contributions investigated the phenomenon from an associational perspective. For instance, Zhou et al. (2021) investigates the association between PM \(_{2.5}\) levels during the 2020 wildfires and the number of COVID-19 cases and deaths by exploiting innovative data sources such as satellite data. In this short paper, we go beyond association and provide a causal analysis using the data from Zhou et al. (2021). We design the study and exploit the recent Synthetic Control for Staggered Adoption method (Ben-Michael et al., 2022) to estimate the treatment effect on treated units of wildfires on COVID-19 cases. We leave out the analysis on deaths for brevity. Results are encouraging but not conclusive.